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Optimal sizing and control of energy storage in wind power-rich distribution networks

机译:风力发电丰富的配电网中储能的最佳尺寸和控制

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This paper presents a planning framework to find the minimum storage sizes (power and energy) at multiple locations in distribution networks to reduce curtailment from renewable distributed generation (DG), specifically wind farms, while managing congestion and voltages. A two-stage iterative process is adopted in this framework. The first stage uses a multi-period AC optimal power flow (OPF) across the studied horizon to obtain initial storage sizes considering hourly wind and load profiles. The second stage adopts a high granularity minute-by-minute control driven by a mono-period bi-level AC OPF to tune the first-stage storage sizes according to the actual curtailment. Congestion and voltages are managed through the optimal control of storage (active and reactive power), on-load tap changers (OLTCs), DG power factor, and DG curtailment as last resort. The proposed storage planning framework is applied to a real 33-kV network from the North West of England over one week. The results highlight that by embedding high granularity control aspects into planning, it is possible to more accurately size storage facilities. Moreover, intelligent management of further flexibility (i.e., OLTCs, storage, and DG power factor control) can lead to much smaller storage capacities. This, however, depends on the required level of curtailment.
机译:本文提出了一个规划框架,旨在在配电网络中的多个位置找到最小的存储容量(电力和能源),以减少可再生分布式发电(DG)(特别是风电场)的能耗,同时管理拥堵和电压。在此框架中采用了两阶段的迭代过程。第一阶段使用跨研究水平的多周期AC最佳潮流(OPF),以获得考虑每小时风速和负荷曲线的初始存储容量。第二阶段采用单周期双级AC OPF驱动的高粒度分分钟控制,以根据实际缩减来调整第一阶段的存储大小。可以通过对存储(有功和无功功率),有载分接开关(OLTC),DG功率因数和DG缩减的最佳控制进行最佳控制来管理拥塞和电压。拟议的存储计划框架将在一周内应用于英格兰西北部的实际33 kV网络。结果表明,通过将高粒度控制方面嵌入计划中,可以更精确地确定存储设施的大小。此外,进一步管理的智能管理(即OLTC,存储和DG功率因数控制)可以导致更小的存储容量。但是,这取决于所需的削减程度。

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